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Multiparametric MRI of the pancreas can potentially benefit from the fusion of multiple acquisitions. However, its small, irregular structure often results in poor organ alignment between acquisitions, potentially leading to inaccurate quantification. Recent studies using UK Biobank data have proposed using pancreas segmentation from a 3D volumetric scan to extract a region of interest in 2D quantitative maps. A limitation of these studies is that potential misalignment between the volumetric and single-slice scans has not been considered. In this paper, we report a slice-to-volume registration (SVR) method with multimodal similarity that aligns the UK Biobank pancreatic 3D acquisitions with the 2D acquisitions, leading to more accurate downstream quantification of an individual’s pancreas T1. We validate the SVR method on a challenging UK Biobank subset of N = 50, using both direct and indirect performance metrics.

The UK Biobank imaging sub-study enables large-scale measurement of pancreas volume, an important biomarker in metabolic disease, including diabetes. Previous methods utilised a pancreas-specific (PS) 3D MRI UK Biobank acquisition to automatically measure pancreas volume. This may lead to a clinically significant underestimation of volume, due to partial coverage of the pancreas in these acquisitions. To address this, we propose a pipeline for the accurate measurement of pancreas volume using stitched whole-body (WB) 3D MRI UK Biobank acquisitions and deep learning-based segmentation. We implement and compare the performance of six different U-Net-like model architectures, leveraging attention layers, recurrent layers, and residual blocks. Furthermore, we investigate pancreas volumetry in 42,313 subjects, separated by sex, and present novel results concerning the change in pancreas volume throughout the course of a day (diurnal variation). To the best of our knowledge, this is the largest pancreas volumetry study to date and the first to propose a pipeline using the whole-body UK Biobank MRI acquisitions to measure pancreas volume.

Accepted manuscript, This report presents the methods and results of the Thoracic Auto-Segmentation Challenge organized at the 2017 Annual Meeting of American Association of Physicists in Medicine. The purpose of the challenge was to provide a benchmark dataset and platform for evaluating performance of autosegmentation methods of organs at risk (OARs) in thoracic CT images., The challenge organizers would like to thank Artem Mamonov and Andrew Beers from Harvard Medical School for providing valuable support in creating the challenge scoring system on the challenge website, Kirk Smith and Tracy Nolan from University of Arkansas for Medical Sciences for data curation to TCIA, Tim Lustberg from Maastro Clinic for data collection, and AAPM for sponsoring this challenge. Of the challenge participants, Brent van der Heyden would like to thank Frank Verhaegen and Mark Podesta for valuable discussions; Bruno Oliveira would like to thank Sandro Queirós, Pedro Morais, Helena R. Torres, Jaime C. Fonseca, João Gomes-Fonseca, and João L. Vilaça for all the contributions to his work; Leonid Zamdborg would like to acknowledge Thomas M Guerrero and Edward Castillo. This project was support in part by the CPRIT (Cancer Prevention Research Institute of Texas) grant No. RP110562-P2, the National Institutes of Health Cancer Center Support (Core) grant No. CA016672 to the University of Texas MD Anderson Cancer Center, and in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Contract No. HHSN261200800001E. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government, info:eu-repo/semantics/publishedVersion

Background Preterm infants are at high risk of diffuse white matter injury and adverse neurodevelopmental outcome. The multiple hit hypothesis suggests that the risk of white matter injury increases with cumulative exposure to multiple perinatal risk factors. Our aim was to test this hypothesis in a large cohort of preterm infants using diffusion weighted magnetic resonance imaging (dMRI). Methods We studied 491 infants (52% male) without focal destructive brain lesions born at, Highlights • White matter injury was assessed in 491 preterm infants at term equivalent age. • Aberrant white matter development was associated with several perinatal factors. • Our findings support the multiple hit hypothesis for preterm brain injury.

See Duerden and Thompson (doi:10.1093/brain/awz421) for a scientific commentary on this article. Interpreting neonatal clinical neuroimaging is challenging due to rapid brain maturation over short periods. O'Muircheartaigh et al. use statistical modelling to build growth curves of tissue intensity and shape, and show that focal white matter injuries present as deviations from these curves in individual neonates., Premature birth occurs during a period of rapid brain growth. In this context, interpreting clinical neuroimaging can be complicated by the typical changes in brain contrast, size and gyrification occurring in the background to any pathology. To model and describe this evolving background in brain shape and contrast, we used a Bayesian regression technique, Gaussian process regression, adapted to multiple correlated outputs. Using MRI, we simultaneously estimated brain tissue intensity on T1- and T2-weighted scans as well as local tissue shape in a large cohort of 408 neonates scanned cross-sectionally across the perinatal period. The resulting model provided a continuous estimate of brain shape and intensity, appropriate to age at scan, degree of prematurity and sex. Next, we investigated the clinical utility of this model to detect focal white matter injury. In individual neonates, we calculated deviations of a neonate’s observed MRI from that predicted by the model to detect punctate white matter lesions with very good accuracy (area under the curve > 0.95). To investigate longitudinal consistency of the model, we calculated model deviations in 46 neonates who were scanned on a second occasion. These infants’ voxelwise deviations from the model could be used to identify them from the other 408 images in 83% (T2-weighted) and 76% (T1-weighted) of cases, indicating an anatomical fingerprint. Our approach provides accurate estimates of non-linear changes in brain tissue intensity and shape with clear potential for radiological use.

International audience; Microglia of the developing brain have unique functional properties but how their activation states are regulated is poorly understood. Inflammatory activation of microglia in the still-developing brain of preterm-born infants is associated with permanent neurological sequelae in 9 million infants every year. Investigating the regulators of microglial activation in the developing brain across models of neuroinflammation-mediated injury (mouse, zebrafish) and primary human and mouse microglia we found using analysis of genes and proteins that a reduction in Wnt/b-catenin signalling is necessary and sufficient to drive a microglial phenotype causing hypomyelination. We validated in a cohort of preterm-born infants that genomic variation in the Wnt pathway is associated with the levels of connectivity found in their brains. Using a Wnt agonist delivered by a blood-brain barrier penetrant microglia-specific targeting nanocarrier we prevented in our animal model the pro-inflammatory microglial activation, white matter injury and behavioural deficits. Collectively, these data validate that the Wnt pathway regulates microglial activation, is critical in the evolution of an important form of human brain injury and is a viable therapeutic target.

Microglia-mediated neuroinflammation is key in numerous brain diseases including encephalopathy of the preterm born infant. Microglia of the still-developing brain have unique properties but little is known of how they regulate their inflammatory activation. This is important information as every year 9 million preterm born infants acquire persisting neurological injuries associated with encephalopathy and we lack strategies to prevent and treat these injuries. Our study of activation state regulators in immature brain microglia found a robust down-regulation of Wnt/β-catenin pathway receptors, ligands and intracellular signalling members in pro-inflammatory microglia. We undertook our studies initially in a mouse model of microglia-mediated encephalopathy including the clinical hallmarks of oligodendrocyte injury and hypomyelination. We purified microglia from this model and applied a genome-wide transcriptomics analysis validated with quantitative profiling. We then verified that down-regulation of the Wnt/β-catenin signalling cascade is sufficient and necessary to drive microglia into an oligodendrocyte-damaging phenotype using multiple pharmacological and genetic approaches in vitro and in vivo in mice and in humans and zebrafish. We also demonstrated that genomic variance in the WNT/β-catenin pathway is associated with the anatomical connectivity phenotype of the human preterm born infant. This integrated analysis of genomics and connectivity, as a surrogate for oligodendrocyte function/myelination, is agnostic to cell type. However, this data indicates that the WNT pathway is relevant to human brain injury and specifically that WNT variants may be useful clinically for injury stratification and prognosis. Finally, we performed a translational experiment using a BBB penetrant microglia-specific targeting 3DNA nanocarrier to deliver a Wnt agonist specifically and directly to microglia in vivo. Increasing the activity of the Wnt/β-catenin pathway specifically in microglia in our model of microglia-mediated encephalopathy was able to reduce microglial pro-inflammatory activation, prevent the typical hypomyelination and also prevent the long-term memory deficit associated with this hypomyelination. In summary, the canonical Wnt/β-catenin pathway regulates microglial activation and up-regulation of this pathway could be a viable neurotherapeutic strategy.

Background and purpose: Contouring of organs at risk (OARS) is an important but time consuming part of radiotherapy treatment planning. The aim of this study was to investigate whether using institutional created software-generated contouring will save time if used as a starting point for manual OAR contouring for lung cancer patients. Material and methods: Twenty CT scans of stage I-Ill NSCLC patients were used to compare user adjusted contours after an atlas-based and deep learning contour, against manual delineation. The lungs, esophagus, spinal cord, heart and mediastinum were contoured for this study. The time to perform the manual tasks was recorded. Results: With a median time of 20 min for manual contouring, the total median time saved was 7.8 min when using atlas-based contouring and 10 min for deep learning contouring. Both atlas based and deep learning adjustment times were significantly lower than manual contouring time for all OARs except for the left lung and esophagus of the atlas based contouring. Conclusions: User adjustment of software generated contours is a viable strategy to reduce contouring time of OARs for lung radiotherapy while conforming to local clinical standards. In addition, deep learning contouring shows promising results compared to existing solutions. (C) 2017 The Authors. Published by Elsevier Ireland Ltd. Radiotherapy and Oncology 126 (2018) 312-317 This is an open access article under the CC BY-NC-ND license (http://creativecommons.orgilicensesiby-nc-nd/4.0/).

In this study, we construct a spatio-temporal surface atlas of the developing cerebral cortex, which is an important tool for analysing and understanding normal and abnormal cortical development. In utero Magnetic Resonance Imaging (MRI) of 80 healthy fetuses was performed, with a gestational age range of 21.7 to 38.9 weeks. Topologically correct cortical surface models were extracted from reconstructed 3D MRI volumes. Accurate correspondences were obtained by applying a joint spectral analysis to cortices for sets of subjects close to a specific age. Sulcal alignment was found to be accurate in comparison to spherical demons, a state of the art registration technique for aligning 2D cortical representations (average Fréchet distance ≈ 0.4 mm at 30 weeks). We construct consistent, unbiased average cortical surface templates, for each week of gestation, from age-matched groups of surfaces by applying kernel regression in the spectral domain. These were found to accurately capture the average cortical shape of individuals within the cohort, suggesting a good alignment of cortical geometry. Each spectral embedding and its corresponding cortical surface template provide a dual reference space where cortical geometry is aligned and a vertex-wise morphometric analysis can be undertaken.

Combining diffusion magnetic resonance imaging and network analysis in the adult human brain has identified a set of highly connected cortical hubs that form a "rich club"--a high-cost, high-capacity backbone thought to enable efficient network communication. Rich-club architecture appears to be a persistent feature of the mature mammalian brain, but it is not known when this structure emerges during human development. In this longitudinal study we chart the emergence of structural organization in mid to late gestation. We demonstrate that a rich club of interconnected cortical hubs is already present by 30 wk gestation. Subsequently, until the time of normal birth, the principal development is a proliferation of connections between core hubs and the rest of the brain. We also consider the impact of environmental factors on early network development, and compare term-born neonates to preterm infants at term-equivalent age. Though rich-club organization remains intact following premature birth, we reveal significant disruptions in both in cortical-subcortical connectivity and short-distance corticocortical connections. Rich club organization is present well before the normal time of birth and may provide the fundamental structural architecture for the subsequent emergence of complex neurological functions. Premature exposure to the extrauterine environment is associated with altered network architecture and reduced network capacity, which may in part account for the high prevalence of cognitive problems in preterm infants.

Introduction Defining connectivity in the human brain signifies a major neuroscientific goal. Advanced imaging techniques have enabled the non-invasive tracing of brain networks to define the human connectome on a millimetre-scale. During early development, the brain undergoes significant changes that are likely represented in the developing connectome, and preterm birth represents a significant environmental risk factor that impacts negatively on early cerebral development. Using tractography to comprehensively map the connections of the thalamocortical unit, we aim to demonstrate that premature extrauterine life due to preterm delivery results in significantly decreased thalamocortical connectivity in the developing human neonate. Methods T1- and T2-weighted magnetic resonance images and 32-direction diffusion tensor images were acquired from 18 healthy term-born neonates (median gestational age: 41+3) and 47 preterm infants (median gestational age: 28+3) scanned at term-equivalent age. Using a novel processing pipeline for tracing connections in the neonatal brain we map and compare the thalamocortical macro-connectome between groups. Results We demonstrate that connections between the thalamus and the frontal cortices, supplementary motor areas, occipital lobe and temporal gyri are significantly diminished in preterm infants (FDR-corrected, p Conclusions This supports the hypothesis that the thalamocortical system is vulnerable following preterm birth and the tractographic framework presented represents a method for analysing system connectivity that can be readily applied to other populations and neural systems.

Cortical maturation was studied in 65 infants between 27 and 46 wk postconception using structural and diffusion magnetic resonance imaging. Alterations in neural structure and complexity were inferred from changes in mean diffusivity and fractional anisotropy, analyzed by sampling regions of interest and also by a unique whole-cortex mapping approach. Mean diffusivity was higher in gyri than sulci and in frontal compared with occipital lobes, decreasing consistently throughout the study period. Fractional anisotropy declined until 38 wk, with initial values and rates of change higher in gyri, frontal and temporal poles, and parietal cortex; and lower in sulcal, perirolandic, and medial occipital cortex. Neuroanatomical studies and experimental diffusion–anatomic correlations strongly suggested the interpretation that cellular and synaptic complexity and density increased steadily throughout the period, whereas elongation and branching of dendrites orthogonal to cortical columns was later and faster in higher-order association cortex, proceeding rapidly before becoming undetectable after 38 wk. The rate of microstructural maturation correlated locally with cortical growth, and predicted higher neurodevelopmental test scores at 2 y of age. Cortical microstructural development was reduced in a dose-dependent fashion by longer premature exposure to the extrauterine environment, and preterm infants at term-corrected age possessed less mature cortex than term-born infants. The results are compatible with predictions of the tension theory of cortical growth and show that rapidly developing cortical microstructure is vulnerable to the effects of premature birth, suggesting a mechanism for the adverse effects of preterm delivery on cognitive function.

OBJECTIVES: To investigate third-trimester extrauterine brain growth and correlate this with clinical risk factors in the neonatal period, using serially acquired brain tissue volumes in a large, unselected cohort of extremely preterm born infants. STUDY DESIGN: Preterm infants (gestational age

Statistical cardiac motion atlases provide a valuable tool for the quantification and evaluation of heart disease. Typically, such atlases are formed and applied using data from the same modality, e.g. magnetic resonance (MR) or 3D ultrasound (US). This paper presents a feasibility study on building a statistical cardiac motion atlas using one modality (MR), and embedding a new patient into this atlas using another modality (US). We aim to simulate a clinical scenario in which a high quality motion atlas is formed using MR data, and a new patient can be related to this atlas using only low cost ultrasound data, which is widely available in clinics. A leave-one-out technique was used to quantify the similarity between MR and US-derived displacement vectors in a reduced dimensional atlas space. Results show that all subjects' embedding errors were statistically significantly lower than the pairwise distances between each of the MR-derived training embedded points. This feasibility study illustrates the potential of combining two sources of information (i.e. MR-US) for comparing subjects' motions in a common space.

Medical imaging has shown that, during early development, the brain undergoes more changes in size, shape and appearance than at any other time in life. A better understanding of brain development requires a spatio-temporal atlas that characterizes the dynamic changes during this period. In this paper we present an approach for constructing a 4D atlas of the developing brain, between 28 and 44 weeks post-menstrual age at time of scan, using T1 and T2 weighted MR images from 204 premature neonates. The method used for the creation of the average 4D atlas utilizes non-rigid registration between all pairs of images to eliminate bias in the atlas toward any of the original images. In addition, kernel regression is used to produce age-dependent anatomical templates. A novelty in our approach is the use of a time-varying kernel width, to overcome the variations in the distribution of subjects at different ages. This leads to an atlas that retains a consistent level of detail at every time-point. Comparisons between the resulting atlas and atlases constructed using affine and non-rigid registration are presented. The resulting 4D atlas has greater anatomic definition than currently available 4D atlases created using various affine and non-rigid registration approaches, an important factor in improving registrations between the atlas and individual subjects. Also, the resulting 4D atlas can serve as a good representative of the population of interest as it reflects both global and local changes. The atlas is publicly available at www.brain-development.org .

The functions of the resting state networks (RSNs) revealed by functional MRI remain unclear, but it has seemed possible that networks emerge in parallel with the development of related cognitive functions. We tested the alternative hypothesis: that the full repertoire of resting state dynamics emerges during the period of rapid neural growth before the normal time of birth at term (around 40 wk of gestation). We used a series of independent analytical techniques to map in detail the development of different networks in 70 infants born between 29 and 43 wk of postmenstrual age (PMA). We characterized and charted the development of RSNs from recognizable but often fragmentary elements at 30 wk of PMA to full facsimiles of adult patterns at term. Visual, auditory, somatosensory, motor, default mode, frontoparietal, and executive control networks developed at different rates; however, by term, complete networks were present, several of which were integrated with thalamic activity. These results place the emergence of RSNs largely during the period of rapid neural growth in the third trimester of gestation, suggesting that they are formed before the acquisition of cognitive competencies in later childhood.

Diffuse white matter injury is common in preterm infants and is a candidate substrate for later cognitive impairment. This injury pattern is associated with morphological changes in deep grey nuclei, the localization of which is uncertain. We test the hypotheses that diffuse white matter injury is associated with discrete focal tissue loss, and that this image phenotype is associated with impairment at 2years. We acquired magnetic resonance images from 80 preterm infants at term equivalent (mean gestational age 29(+6)weeks) and 20 control infants (mean GA 39(+2)weeks). Diffuse white matter injury was defined by abnormal apparent diffusion coefficient values in one or more white matter region (frontal, central or posterior white matter at the level of the centrum semiovale), and morphological difference between groups was calculated from 3D images using deformation based morphometry. Neurodevelopmental assessments were obtained from preterm infants at a mean chronological age of 27.5months, and from controls at a mean age of 31.1months. We identified a common image phenotype in 66 of 80 preterm infants at term equivalent comprising: diffuse white matter injury; and tissue volume reduction in the dorsomedial nucleus of the thalamus, the globus pallidus, periventricular white matter, the corona radiata and within the central region of the centrum semiovale (t=4.42 p

We propose a novel framework for the automatic propagation of a set of manually labeled brain atlases to a diverse set of images of a population of subjects. A manifold is learned from a coordinate system embedding that allows the identification of neighborhoods which contain images that are similar based on a chosen criterion. Within the new coordinate system, the initial set of atlases is propagated to all images through a succession of multi-atlas segmentation steps. This breaks the problem of registering images that are very "dissimilar" down into a problem of registering a series of images that are "similar". At the same time, it allows the potentially large deformation between the images to be modeled as a sequence of several smaller deformations. We applied the proposed method to an exemplar region centered around the hippocampus from a set of 30 atlases based on images from young healthy subjects and a dataset of 796 images from elderly dementia patients and age-matched controls enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI). We demonstrate an increasing gain in accuracy of the new method, compared to standard multi-atlas segmentation, with increasing distance between the target image and the initial set of atlases in the coordinate embedding, i.e., with a greater difference between atlas and image. For the segmentation of the hippocampus on 182 images for which a manual segmentation is available, we achieved an average overlap (Dice coefficient) of 0.85 with the manual reference.

Quantitative research in neuroimaging often relies on anatomical segmentation of human brain MR images. Recent multi-atlas based approaches provide highly accurate structural segmentations of the brain by propagating manual delineations from multiple atlases in a database to a query subject and combining them. The atlas databases which can be used for these purposes are growing steadily. We present a framework to address the consequent problems of scale in multi-atlas segmentation. We show that selecting a custom subset of atlases for each query subject provides more accurate subcortical segmentations than those given by non-selective combination of random atlas subsets. Using a database of 275 atlases, we tested an image-based similarity criterion as well as a demographic criterion (age) in a leave-one-out cross-validation study. Using a custom ranking of the database for each subject, we combined a varying number n of atlases from the top of the ranked list. The resulting segmentations were compared with manual reference segmentations using Dice overlap. Image-based selection provided better segmentations than random subsets (mean Dice overlap 0.854 vs. 0.811 for the estimated optimal subset size, n=20). Age-based selection resulted in a similar marked improvement. We conclude that selecting atlases from large databases for atlas-based brain image segmentation improves the accuracy of the segmentations achieved. We show that image similarity is a suitable selection criterion and give results based on selecting atlases by age that demonstrate the value of meta-information for selection.

Objective: Preterm infants have reduced cerebral tissue volumes in adolescence. This study addresses the question: Is reduced global brain growth in the neonatal period inevitable after premature birth, or is it associated with specific medical risk factors? Methods: Eighty-nine preterm infants at term equivalent age without focal parenchymal brain lesions were studied with 20 full-term control infants. Using a deformation-based morphometric approach, we transformed images to a reference anatomic space, and we used the transformations to calculate whole-brain volume and ventricular volume for each subject. Patterns of volume difference were correlated with clinical data. Results: Cerebral volume is not reduced compared with term born control infants ( p 0.765). Supplemental oxygen requirement at 28 postnatal days is associated with lower cerebral tissue volume at term ( p 0.001), but there were no significant differences in cerebral volumes attributable to perinatal sepsis ( p 0.515) and quantitatively defined diffuse white matter injury ( p 0.183). As expected, the ventricular system is significantly larger in preterm infants at term equivalent age compared with term control infants ( p 0.001). Interpretation: Cerebral volume is not reduced during intensive care for the majority of preterm infants, but prolonged supplemental oxygen dependence is a risk factor for early attenuation of global brain growth. The reduced cerebral tissue volume seen in adolescents born preterm does not appear to be an inevitable association of prematurity, but rather caused by either specific disease during intensive care or factors operating beyond the neonatal period. Ann Neurol 2007;62:185–192 Children and adolescents who were born with very low birth weight (VLBW) appear to have reduced total cerebral tissue volumes compared with age-matched control subjects, 1–3 and alterations in the distribution of gray matter, white matter, and ventricular cerebrospinal fluid (CSF) persist into the third decade of life. 4,5 Many of the morphological abnormalities identified in this population are associated with adverse neurodevelopmental outcomes (see Counsell and Boardman 6 for review). However, an understanding of the causes, nature, and temporal evolution of this abnormal growth pattern remains elusive. In particular, it is unclear to what degree the growth failure leading to reduced brain volume in adolescence takes place in the perinatal period or in later childhood. In a study of preterm infants at term equivalent age, brain volume was reduced compared with infants born at term, 7 but this cohort included infants with

Preterm birth is a leading risk factor for neurodevelopmental and cognitive impairment in childhood and adolescence. The most common known cerebral abnormality among preterm infants at term equivalent age is a diffuse white matter abnormality seen on magnetic resonance (MR) images. It occurs with a similar prevalence to subsequent impairment, but its effect on developing neural systems is unknown. MR images were obtained at term equivalent age from 62 infants born at 24–33 completed weeks gestation and 12 term born controls. Tissue damage was quantified using diffusion-weighted imaging, and deformation-based morphometry was used to make a non-subjective survey of the whole brain to identify significant cerebral morphological alterations associated with preterm birth and with diffuse white matter injury. Preterm infants at term equivalent age had reduced thalamic and lentiform volumes without evidence of acute injury in these regions (t = 5.81, P < 0.05), and these alterations were more marked with increasing prematurity (t = 7.13, P < 0.05 for infants born at less than 28 weeks) and in infants with diffuse white matter injury (t = 6.43, P < 0.05). The identification of deep grey matter growth failure in association with diffuse white matter injury suggests that white matter injury is not an isolated phenomenon, but rather, it is associated with the maldevelopment of remote structures. This could be mediated by a disturbance to corticothalamic connectivity during a critical period in cerebral development. Deformation-based morphometry is a powerful tool for modelling the developing brain in health and disease, and can be used to test putative aetiological factors for injury.

Accurately delineating the brain on magnetic resonance (MR) images of the head is a prerequisite for many neuroimaging methods. Most existing methods exhibit disadvantages in that they are laborious, yield inconsistent results, and/or require training data to closely match the data to be processed. Here, we present pincram, an automatic, versatile method for accurately labelling the adult brain on T1-weighted 3D MR head images. The method uses an iterative refinement approach to propagate labels from multiple atlases to a given target image using image registration. At each refinement level, a consensus label is generated. At the subsequent level, the search for the brain boundary is constrained to the neighbourhood of the boundary of this consensus label. The method achieves high accuracy (Jaccard coefficient > 0.95 on typical data, corresponding to a Dice similarity coefficient of > 0.97) and performs better than many state-of-the-art methods as evidenced by independent evaluation on the Segmentation Validation Engine. Via a novel self-monitoring feature, the program generates the "success index," a scalar metadatum indicative of the accuracy of the output label. Pincram is available as open source software.

We automatically quantify patterns of normal cortical folding in the developing fetus from in utero MR images (N=80) over a wide gestational age (GA) range (21.7 to 38.9weeks). This work on data from healthy subjects represents a first step towards characterising abnormal folding that may be related to pathology, facilitating earlier diagnosis and intervention. The cortical boundary was delineated by automatically segmenting the brain MR image into a number of key structures. This utilised a spatio-temporal atlas as tissue priors in an expectation-maximization approach with second order Markov random field (MRF) regularization to improve the accuracy of the cortical boundary estimate. An implicit high resolution surface was then used to compute cortical folding measures. We validated the automated segmentations with manual delineations and the average surface discrepancy was of the order of 1mm. Eight curvature-based folding measures were computed for each fetal cortex and used to give summary shape descriptors. These were strongly correlated with GA (R(2)=0.99) confirming the close link between neurological development and cortical convolution. This allowed an age-dependent non-linear model to be accurately fitted to the folding measures. The model supports visual observations that, after a slow initial start, cortical folding increases rapidly between 25 and 30weeks and subsequently slows near birth. The model allows the accurate prediction of fetal age from an observed folding measure with a smaller error where growth is fastest. We also analysed regional patterns in folding by parcellating each fetal cortex using a nine-region anatomical atlas and found that Gompertz models fitted the change in lobar regions. Regional differences in growth rate were detected, with the parietal and posterior temporal lobes exhibiting the fastest growth, while the cingulate, frontal and medial temporal lobes developed more slowly.

Diffuse optical tomography is most accurate when an individual's MRI data can be used as a spatial prior for image reconstruction and for visualization of the resulting images of changes in oxy- and deoxy-hemoglobin concentration. As this necessitates an MRI scan to be performed for each study, which undermines many of the advantages of diffuse optical methods, the use of registered atlases to model the individual's anatomy is becoming commonplace. Infant studies require carefully age-matched atlases because of the rapid growth and maturation of the infant brain. In this paper, we present a 4D neonatal head model which, for each week from 29 to 44 weeks post-menstrual age, includes: 1) a multi-layered tissue mask which identifies extra-cerebral layers, cerebrospinal fluid, gray matter, white matter, cerebellum and brainstem, 2) a high-density tetrahedral head mesh, 3) surface meshes for the scalp, gray-matter and white matter layers and 4) cranial landmarks and 10-5 locations on the scalp surface. This package, freely available online at www.ucl.ac.uk/medphys/research/4dneonatalmodel can be applied by users of near-infrared spectroscopy and diffuse optical tomography to optimize probe locations, optimize image reconstruction, register data to cortical locations and ultimately improve the accuracy and interpretation of diffuse optical techniques in newborn populations.

Cerebral white-matter injury is common in preterm-born infants and is associated with neurocognitive impairments. Identifying the pattern of connectivity changes in the brain following premature birth may provide a more comprehensive understanding of the neurobiology underlying these impairments. Here, we characterize whole-brain, macrostructural connectivity following preterm delivery and explore the influence of age and prematurity using a data-driven, nonsubjective analysis of diffusion magnetic resonance imaging data. T1- and T2-weighted and -diffusion MRI were obtained between 11 and 31 months postconceptional age in 49 infants, born between 25 and 35 weeks postconception. An optimized processing pipeline, combining anatomical, and tissue segmentations with probabilistic diffusion tractography, was used to map mean tract anisotropy. White-matter tracts where connection strength was related to age of delivery or imaging were identified using sparse-penalized regression and stability selection. Older children had stronger connections in tracts predominantly involving frontal lobe structures. Increasing prematurity at birth was related to widespread reductions in connection strength in tracts involving all cortical lobes and several subcortical structures. This nonsubjective approach to mapping whole-brain connectivity detected hypothesized changes in the strength of intracerebral connections during development and widespread reductions in connectivity strength associated with premature birth.

Diffusion tensor imaging, tractography and the subsequent derivation of network measures are becoming an established approach in the exploration of brain connectivity. However, no gold standard exists in respect to how the brain should be parcellated and therefore a variety of atlas- and random-based parcellation methods are used. The resulting challenge of comparing graphs with differing numbers of nodes and uncertain node correspondences necessitates the use of normalisation schemes to enable meaningful intra- and inter-subject comparisons. This work proposes methods for normalising brain network measures using random graphs. We show that the normalised measures are locally stable over distinct random parcellations of the same subject and, applying it to a neonatal serial diffusion MRI data set, we demonstrate their potential in characterising changes in brain connectivity during early development.

We propose a novel framework for longitudinal registration which can handle large intra-subject anatomical variations. The framework exploits freely available spatio-temporal atlases, which can aid the longitudinal registration process as it provides prior information about the missing anatomical evolution between two scans taken over large time-interval. The spatio-temporal atlas is used to develop an approach to carry out longitudinal registrations via atlas propagation. We carried out evaluation experiments using 50 neonatal subjects with two scans taken at different time-intervals. We used different metrics, including intensity similarity and segmentation overlap, to assess the registration performance. The proposed registration framework outperforms direct registration by providing an accurate and consistent registration, particularly when the time-interval between scans increases. We refer to our approach as a framework for Longitudinal Image registration via Spatio-temporal Atlases (LISA).

Preterm birth is associated with abnormal brain development and long-term neurodevelopmental impairment. Quantitative magnetic resonance (MR) studies of preterm brain injury have focused on morphological features such as shape and volume and on measures of tissue microstructure obtained from diffusion tensor imaging. In this work, we focus on longitudinal changes in signal intensity, which can offer a useful marker for mapping developmental changes. The proposed analysis framework utilises spatial normalization, intensity normalization and kernel regression. Changes over time in T1- and T2- weighted signal intensity were measured in subcortical grey matter and white matter. The study shows that quantitative signal change analysis on a large cohort is feasible, and that it can serve as a marker for developmental brain changes, both normal and abnormal, which might ultimately lead to a better understanding of the trajectory of early brain maturation.

Probabilistic atlases are widely used in the neuroscience community as a tool for providing a standard space for comparison of subjects and as tissue priors used to enhance the intensity-based classification of brain MRI. Most efforts so far have focused on static brain atlases either for adult or pediatric cohorts. In contrast to the adult brain the rapid growth of the neonatal brain requires an age-specific spatial probabilistic atlas to provide suitable anatomical and structural information. In this paper we describe a 4D probabilistic atlas that allows dynamic generation of prior tissue probability maps for any chosen stage of neonatal brain development between 29 and 44 gestational weeks. The atlas is created from the segmentations of 142 neonatal subjects at different ages using a kernel-based regression method and provides prior tissue probability maps for six structures - cortex, white matter, subcortical grey matter, brainstem, cerebellum and cerebro-spinal fluid. The atlas is publicly available at www.brain-development.org.

Probabilistic atlases have been established in the literature as a standard tool for enhancing the intensity-based classification of brain MRI. The rapidly growing neonatal brain requires an age-specific spatial probabilistic atlas to guide the segmentation process. In this paper we describe a method for dynamically creating a probabilistic atlas for any chosen stage of neonatal brain development. The atlas is created from the segmentations of 153 subjects of different ages using a kernel regression method. For any given age, an intensity template as well as the corresponding tissue probability maps with the correct sizes and shapes of the structures can be dynamically generated. The resulting atlas provides prior tissue probability maps for six structures - cortex, white matter, subcortical gray matter, brainstem and cerebellum, for ages of 29 to 44 weeks of gestation.

A robust model for the automatic segmentation of human brain images into anatomically defined regions across the human lifespan would be highly desirable, but such structural segmentations of brain MRI are challenging due to age-related changes. We have developed a new method, based on established algorithms for automatic segmentation of young adults' brains. We used prior information from 30 anatomical atlases, which had been manually segmented into 83 anatomical structures. Target MRIs came from 80 subjects (~12 individuals/decade) from 20 to 90 years, with equal numbers of men, women; data from two different scanners (1.5T, 3T), using the IXI database. Each of the adult atlases was registered to each target MR image. By using additional information from segmentation into tissue classes (GM, WM and CSF) to initialise the warping based on label consistency similarity before feeding this into the previous normalised mutual information non-rigid registration, the registration became robust enough to accommodate atrophy and ventricular enlargement with age. The final segmentation was obtained by combination of the 30 propagated atlases using decision fusion. Kernel smoothing was used for modelling the structural volume changes with aging. Example linear correlation coefficients with age were, for lateral ventricular volume, r male =0.76, r female =0.58 and, for hippocampal volume, r male =-0.6, r female =-0.4 (allρ

We present a method for the construction of patient-specific atlases of the brain. Traditional atlases of the brain aim to characterize the variability of a population of subjects. A common approach is to average the anatomies of a population after alignment to a common coordinate system. Subjects are typically given equal weights during averaging which results in atlases that are population-specific rather than subject specific. In this paper we propose a method for the construction of patient-specific atlas for a given query subject from a large population cohort. During the atlas construction we compute the similarity between the query subject and the subjects in the population cohort. This similarity measure can be based on image similarity or other meta-information (e.g. sex, age, ethnicity, medical history, etc). We show an example of the construction of brain atlases for different ages using a cohort of 575 subjects between the ages of 18 and 80.

The aim of this paper is to investigate techniques that can identify and quantify cross-sectional differences and longitudinal changes in vivo from magnetic resonance images of murine models of brain disease. Two different approaches have been compared. The first approach is a segmentation-based approach: Each subject at each time point is automatically segmented into a number of anatomical structures using atlas-based segmentation. This allows cross-sectional and longitudinal analyses of group differences on a structure-by-structure basis. The second approach is a deformation-based approach: Longitudinal changes are quantified by the registration of each subject's follow-up images to that subject's baseline image. In addition the baseline images can be registered to an atlas allowing voxel-wise analysis of cross-sectional differences between groups. Both approaches have been tested on two groups of mice: A transgenic model of Alzheimer's disease and a wild-type background strain, using serial imaging performed over the age range from 6-14 months. We show that both approaches are able to identify longitudinal and cross-sectional differences. However, atlas-based segmentation suffers from the inability to detect differences across populations and across time in regions which are much smaller than the anatomical regions. In contrast to this, the deformation-based approach can detect statistically significant differences in highly localized areas.

This work describes and compares two different methods for identifying growth patterns in preterm infants during the second year of development. One method is based on creating anatomical atlases from the population of subjects within each timepoint and using the transformation between atlases at different timepoints to create average volume change maps. The second method uses multiple longitudinal intra-subject registrations to produce individual volume change maps. These maps are then transformed into a common coordinate system and averaged with the method used for atlas creation. We show that there is a reasonable level of agreement between the two methods and both generate plausible growth patterns implying that either could be used to track development during this period of growth.

Segmentations of MR images of the human brain can be generated by propagating an existing atlas label volume to the target image. By fusing multiple propagated label volumes, the segmentation can be improved. We developed a model that predicts the improvement of labelling accuracy and precision based on the number of segmentations used as input. Using a cross-validation study on brain image data as well as numerical simulations, we verified the model. Fit parameters of this model are potential indicators of the quality of a given label propagation method or the consistency of the input segmentations used.

Background. Diffuse white matter disease, which can be seen on MRI as Diffuse Excessive High Signal Intensity (DEHSI) and quantified using apparent diffusion coefficient (ADC) values is the most common abnormality in preterm infants at term equivalent age. We used a computational anatomic approach to survey the brain for structural alterations associated diffuse white matter disease.